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Intelligent Transportation Systems, IEEE Transactions on

Issue 2 • Date June 2013

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Displaying Results 1 - 25 of 57
  • Table of contents

    Page(s): C1 - C2
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  • IEEE Transactions on Intelligent Transportation Systems publication information

    Page(s): C2
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  • A Review of IEEE T-ITS: The 2013 Survey Result

    Page(s): 501 - 510
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  • Discrete Fourier Transformation for Seasonal-Factor Pattern Classification and Assignment

    Page(s): 511 - 516
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (616 KB) |  | HTML iconHTML  

    This paper introduces a data mining method to investigate the relationship between seasonal factors (SFs) and land use characteristics for urban areas in Florida through discrete Fourier transformation (DFT). First, DFT is applied to discover seasonal variation patterns, and two typical patterns were identified. Second, linear regression is used to determine influential variables, and a weighted similarity method derived from the amplitude of each DFT wave is applied for the SF assignment. The results obtained by DFT demonstrate promising assignment accuracy with a mean absolute percentage error of 4.27% for all data and 3.96% for the low seasonal household percentage subclass. View full abstract»

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  • Heuristic Algorithms for Constructing Transporter Pools in Container Terminals

    Page(s): 517 - 526
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1265 KB) |  | HTML iconHTML  

    In port container terminals, containers are transported between vessels and storage blocks by transporters. To improve the utilization of transporters and the operational efficiency of container terminals, the pooling strategy is widely applied, and transporters in the same pool are shared by a group of quay cranes (QCs). This paper compares various strategies for constructing the pools: one pool for each QC, one pool for all the QCs deployed to each vessel, one pool for all the QCs for multiple adjacent vessels, and one pool for all the operating QCs in the terminal. Various heuristic algorithms (HAs) to construct pools of transporters are suggested and evaluated in terms of the total delay time of QC operation and the total travel distance of transporters. In addition, opportunities for dual-command-cycle operation are analyzed for each of these heuristic rules by using different data sets of QC operations. Various scenarios of QC operation are generated, and the HAs are compared in terms of their performance through a simulation study. View full abstract»

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  • Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection

    Page(s): 527 - 538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1359 KB) |  | HTML iconHTML  

    A key module of modern advanced driver-assistance systems (ADASs) is the road detector, which has to be robust, even under adverse conditions. The ultimate goal of such a system, which uses only visual information acquired from a color video camera, is to classify each frame pixel as belonging to the road or not. In this direction, this paper proposes a new fully automatic algorithm that combines both time and spatial information using the efficient random-walker algorithm (RWA) as a segmentation tool. A novel technique for automatic seed selection is proposed, utilizing features derived from a shadow-resistant optical flow estimator using the $c_{1}$ channel of the $c_{1}c_{2}c_{3}$ color space, along with a priori information and previous frame segmentation results. The proposed system is qualitatively assessed using video sequences in both typical and adverse conditions, including heavy traffic, shadows, tunnels, rain, night, etc. It is also quantitatively compared with previous efforts on a publicly available manually annotated onboard video database, providing superior results. View full abstract»

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  • Probabilistic Long-Term Vehicle Motion Prediction and Tracking in Large Environments

    Page(s): 539 - 552
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    Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can easily be achieved by providing vehicles with a constant communication link to a control center and having the vehicles broadcast their position. The problem dramatically changes when vehicles operate within a large environment of potentially hundreds of square kilometers and in difficult terrain. This paper presents algorithms for long-term vehicle motion prediction and tracking based on a multiple-model approach. It incorporates a probabilistic vehicle model that includes the structure of the environment. The prediction algorithm evaluates the vehicle position using acceleration, speed, and timing profiles built for the particular environment and considers the probability that the vehicle will stop. A limited number of data collection points distributed around the field are used to update the vehicle position estimate when in communication range, and prediction is used at points in between. A particle filter is used to estimate the vehicle position using both positive and negative information (whether communication is possible) in the fusion stage. The algorithms presented are validated with experimental results using data collected from a large-scale mining operation. View full abstract»

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  • Improving Accuracy of the Vehicle Attitude Estimation for Low-Cost INS/GPS Integration Aided by the GPS-Measured Course Angle

    Page(s): 553 - 564
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    This paper presents a method using the Global Positioning System (GPS)-measured course angle to improve the accuracy of the vehicle attitude estimation for low-cost inertial navigation system/GPS (INS/GPS) integration. Observability properties of the error states in the low-cost integration navigation system are first analyzed, indicating that the attitude estimation is severely affected by vehicle maneuvers, particularly the yaw angle. The pitch and roll angles are strongly observed; hence, the observability of these two angles is nearly free of influence caused by vehicle maneuvers, and these two angles can be accurately estimated. To improve the yaw-angle estimation, we propose a cascaded Kalman filter to deal with the yaw angle separately with the aid of the GPS-measured course angle. Additionally, two switching rules are established to remove the influence caused by the sideslip angle and GPS noise. The experimental results validate the observability analysis of the low-cost INS/GPS system and show that the proposed attitude estimation method can effectively improve the accuracy of the vehicle attitude estimation, suggesting that this technique is a viable candidate for many control applications used in cars. View full abstract»

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  • Performance-Based Classification of Occupant Posture to Reduce the Risk of Injury in a Collision

    Page(s): 565 - 573
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    This study numerically investigates the development of an adaptive restraint system based on precrash classification of occupant posture. A catalog of restraint laws optimized for nine postures uniformly distributed in posture space is employed. First, the performance of each restraint law is globally assessed by performing crash simulations in a parametric fashion throughout the entire posture space. Then, restraint systems with catalogs (RSCs) with various numbers of restraint laws are evaluated in terms of injury cost with respect to a restraint system optimized with respect to a nominal posture (RSN). Parametric and nonparametric supervised classifiers are developed for each catalog, and their performances are analyzed. A catalog with the optimized laws of two out-of-position postures (central and leaning left) showed high performance in terms of reduced injury cost with respect to optimum performance for two distinct validation sets (25.3%/21.6% with statistical classifiers versus 26%/23.8% optimum performance). The percent injury reduction increased as the number of classes was increased but had diminishing returns going from five to nine restraint laws (28%/24.2% with statistical classifiers versus 30.4%/29.1% optimum reduction). The results of this study indicated that restraint systems with performance-based classes perform better than restraint systems with region-based classes. Expanding the number of restraint laws and developing new classification algorithms may further improve the performance of adaptive restraint systems. View full abstract»

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  • A Genetic Programming Model for Real-Time Crash Prediction on Freeways

    Page(s): 574 - 586
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1072 KB) |  | HTML iconHTML  

    This paper aimed at evaluating the application of the genetic programming (GP) model for real-time crash prediction on freeways. Traffic, weather, and crash data used in this paper were obtained from the I-880N freeway in California, United States. The random forest (RF) technique was conducted to select the variables that affect crash risk under uncongested and congested traffic conditions. The GP model was developed for each traffic state based on the candidate variables that were selected by the RF technique. The traffic flow characteristics that contribute to crash risk were found to be quite different between congested and uncongested traffic conditions. This paper applied the receiver operating characteristic (ROC) curve to evaluate the prediction performance of the developed GP model for each traffic state. The validation results showed that the prediction performance of the GP models were satisfactory. The binary logit model was also developed for each traffic state using the same training data set. The authors compared the ROC curve of the GP model and the binary logit model for each traffic state. The GP model produced better prediction performance than did the binary logit model for each traffic state. The GP model was found to increase the crash prediction accuracy under uncongested traffic conditions by an average of 8.2% and to increase the crash prediction accuracy under congested traffic conditions by an average of 4.9%. View full abstract»

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  • Integrated Model Predictive Traffic and Emission Control Using a Piecewise-Affine Approach

    Page(s): 587 - 598
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1010 KB) |  | HTML iconHTML  

    This paper addresses the computational intractability of traffic control when applying the integrated METANET freeway traffic model and the VT-macro emission model in a model-based predictive control (MPC) framework. To facilitate real-time implementation, a piecewise-affine (PWA) approximation of the nonlinear METANET model is proposed. While a direct MPC approach based on the full PWA model is intractable for online applications, a conversion to a mixed-logical dynamical (MLD) model description is made instead. The resulting MLD-MPC problem, which is written as a mixed-integer linear program (MILP), can be solved much more efficiently as it does not explicitly state all model equations for each particular region. As a benchmark, the computational efficiency and accuracy of the MLD-MPC approach is tested on a case study including variable speed limits and a metered on-ramp while optimizing the total time spent (TTS) and taking into account emissions and fuel consumption of the vehicles. The performance is evaluated against the original nonlinear and nonconvex MPC problem and shows an improved computational speed at the cost of some deviation in the cost function values. View full abstract»

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  • Demonstration of In-Car Doppler Laser Radar at 1.55  \mu\hbox {m} for Range and Speed Measurement

    Page(s): 599 - 607
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    Laser radar provides better spatial resolution than millimeter-wave radar (MWR) due to the high directivity of the laser beam. However, commercial in-car laser radar approximates the target speed by a range differentiation method, which has the problems of excessive time consumption and the introduction of large errors. In this paper, a new Doppler laser radar scheme for simultaneously measuring target range and speed in automotive applications is presented. The scheme includes a new laser radar architecture, a new method to modulate the transmitted signal, and a method for calculating the range and speed from the signal returned from the target. The length of the transmitted signal is several microseconds, giving the possibility of realizing a high scan speed in automotive applications. In addition, simulations based on Simulink/Matlab were carried out to validate the proposed scheme. Finally, an experimental demonstration of simultaneous range and speed measurements was performed. The moving target for the experiment was a highly reflecting sheet attached to an electric grinder. View full abstract»

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  • Augmented Reality Experiment: Drivers' Behavior at an Unsignalized Intersection

    Page(s): 608 - 617
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1131 KB) |  | HTML iconHTML  

    Applying new technologies to traffic engineering studies has become more urgent due to the high cost and risk associated with ordinary in-the-field testing. Augmented reality (AR) is one of those technologies, in which virtual (computer-generated) objects are added to the real scene in a way that the user cannot distinguish between real and virtual objects in the final scene. Adding virtual objects (people, vehicles, hazards, and other objects) to the normal view can provide a safe realistic environment for testing driving performance under different scenarios. This paper presents two systems, i.e., AR vehicle (ARV) and offline AR simulator (OARSim) systems, and uses them to study the left-turn driving behavior at an unsignalized intersection for drivers with different characteristics. Two experiments were performed: one using the ARV system installed in a vehicle and another using the OARSim system installed in the laboratory. Quantitative measurements of left-turn drivers' behaviors were recorded. There was no significant gender effect on all measured parameters in both experiments. Older drivers selected larger gaps and used smaller acceleration rates to turn left than younger drivers in both experiments. The conservative driving attitude of older drivers indicates the potential presence of reduced driving ability of the elderly. While left-turn times using the ARV system were not significantly affected by drivers' age, older drivers took longer time to complete the left-turn maneuver than younger drivers using the OARSim did. Results from this study supported the feasibility and validity of the proposed systems and showed promise for these systems to be used as surrogates to in-the-field testing for safety and operation aspects of transportation research. View full abstract»

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  • Estimation of Dynamic Origin–Destination Matrices Using Linear Assignment Matrix Approximations

    Page(s): 618 - 626
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (974 KB) |  | HTML iconHTML  

    This paper presents a general solution scheme for the problem of offline estimation of dynamic origin-destination (OD) demand matrices using traffic counts on some of the network links and historical demand information. The proposed method uses linear approximations of the assignment matrix, which maps the OD demand to link traffic counts. Several iterative algorithms that are based on this scheme are developed. The various algorithms are implemented in a tool that uses the mesoscopic traffic simulation model Mezzo to conduct network loadings. A case study network in Stockholm, Sweden, is used to test the proposed algorithms and to compare their performance with current state-of-the-art methods. The results demonstrate the applicability of the proposed methodology to efficiently obtain dynamic OD demand estimates for large and complex networks and that, computationally, this methodology outperforms existing methods. View full abstract»

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  • Emergency Management of Urban Rail Transportation Based on Parallel Systems

    Page(s): 627 - 636
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1537 KB) |  | HTML iconHTML  

    Integrating artificial systems, computational experiments, and parallel execution (ACP) is an effective approach to modeling, simulating, and intervening real complex systems. Emergency response is an important issue in the operation of urban rail transport systems for ensuring the safety of people and property. Inspired by the ACP method, this paper introduces a basic framework of parallel control and management (PCM) for emergency response of urban rail transportation systems. The proposed framework is elaborated from three interdependent aspects: Points, Lines, and Networks. Points represent the modeling of urban rail stations, Lines describe the microscopic characteristics of urban rail connections between designated stations, and Networks present the macroscopic properties of all the urban rail connections. Based on the given framework, a series of parallel experiments, which were impossible to achieve in real systems, can now be conducted in the constructed artificial system. Furthermore, the constructed artificial system can be used to test and develop effective emergency control and management strategies for real rail transport systems. Therefore, this proposed framework will be able to enhance the reliability, security, robustness, and maneuverability of urban rail transport systems in case of an emergency. View full abstract»

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  • A Probabilistic Framework for Decision-Making in Collision Avoidance Systems

    Page(s): 637 - 648
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    This paper is concerned with the problem of decision-making in systems that assist drivers in avoiding collisions. An important aspect of these systems is not only assisting the driver when needed but also not disturbing the driver with unnecessary interventions. Aimed at improving both of these properties, a probabilistic framework is presented for jointly evaluating the driver acceptance of an intervention and the necessity thereof to automatically avoid a collision. The intervention acceptance is modeled as high if it estimated that the driver judges the situation as critical, based on the driver's observations and predictions of the traffic situation. One advantage with the proposed framework is that interventions can be initiated at an earlier stage when the estimated driver acceptance is high. Using a simplified driver model, the framework is applied to a few different types of collision scenarios. The results show that the framework has appealing properties, both with respect to increasing the system benefit and to decreasing the risk of unnecessary interventions. View full abstract»

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  • Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications

    Page(s): 649 - 659
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    Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols. View full abstract»

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  • Stochastic Approach for Short-Term Freeway Traffic Prediction During Peak Periods

    Page(s): 660 - 672
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (881 KB) |  | HTML iconHTML  

    Using a stochastic approach, this paper explores and models the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions during peak periods and then applies the models to short-term traffic speed prediction. The speed transition probabilities are estimated from real-world 30-s speed data over a six-year period at three different locations along the 38-mi corridor of Interstate 4 (I-4) in Orlando, FL. The cumulative negative/positive transition probabilities and expected values are derived from the transition probabilities and fitted using logistic and exponential models, respectively. The expected values associated with the most likely transition of speed are then derived from the fitted models and used for predicting speed. Each predicted speed is also associated with a probability value, indicating the chance of observing the occurrence of such transition. The prediction performance was compared for three methods using the root mean square errors (RMSEs). The weighted average method was very close to the higher probability method in most cases. For the two probabilistic methods, the performance was slightly better for the morning peak periods than the evening peak period or all data combined. While the prediction performance of the probabilistic models was comparable with those of other methods found in the literature, the probabilistic approach based on the higher probability provides estimates of the associated probability with each prediction. This provides a measure of confidence in the predicted values before such information is disseminated to the public by traffic agencies. View full abstract»

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  • Accurate Ego-Vehicle Global Localization at Intersections Through Alignment of Visual Data With Digital Map

    Page(s): 673 - 687
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1584 KB) |  | HTML iconHTML  

    This paper proposes a method for achieving improved ego-vehicle global localization with respect to an approaching intersection, which is based on the alignment of visual landmarks perceived by the on-board visual system, with the information from a proposed extended digital map (EDM). The visual system relies on a stereovision system that provides a detailed 3-D description of the environment, including road landmark information (lateral lane delimiters, painted traffic signs, curbs, and stop lines) and dynamic environment information (other vehicles). An EDM is proposed, which enriches the standard map information with a detailed description of the intersection required for current lane identification, landmark alignment, and ego-vehicle accurate global localization. A novel approach for lane-delimiter classification, which is necessary for the lane identification, is also presented. An original solution for identifying the current lane, combining visual and map information with the help of a Bayesian network (BN), is proposed. Extensive experiments have been performed, and the results are evaluated with a Global Navigation Satellite System of high accuracy (2 cm). The achieved global localization accuracy is of submeter level, depending on the performance of the stereovision system. View full abstract»

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  • Automated Real-Time Detection of Potentially Suspicious Behavior in Public Transport Areas

    Page(s): 688 - 699
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1820 KB) |  | HTML iconHTML  

    Detection of suspicious activities in public transport areas using video surveillance has attracted an increasing level of attention. In general, automated offline video processing systems have been used for post-event analysis, such as forensics and riot investigations. However, very little has been achieved regarding real-time event recognition. In this paper, we introduce a framework that processes raw video data received from a fixed color camera installed at a particular location, which makes real-time inferences about the observed activities. First, the proposed framework obtains 3-D object-level information by detecting and tracking people and luggage in the scene using a real-time blob matching technique. Based on the temporal properties of these blobs, behaviors and events are semantically recognized by employing object and interobject motion features. A number of types of behavior that are relevant to security in public transport areas have been selected to demonstrate the capabilities of this approach. Examples of these are abandoned and stolen objects, fighting, fainting, and loitering. Using standard public data sets, the experimental results presented here demonstrate the outstanding performance and low computational complexity of this approach. We also discuss the advantages over other approaches in the literature. View full abstract»

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  • Modeling and Delay Analysis of a Retransmission-Based Bundle Delivery Scheme for Intermittent Roadside Communication Networks

    Page(s): 700 - 708
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    This paper proposes a novel bundle delivery scheme (BDS) aimed at achieving a delay-minimal bundle delivery in the context of an intermittent roadside network. The realization of this objective is challenging whenever network information is completely unavailable. The concept of virtual space (VS) presents itself as an efficient solution that allows the source to perform necessary data bundle retransmissions to a subset of arriving vehicles. In turn, these vehicles will secure earlier delivery of the retransmitted bundles to the destination. A thorough empirical performance evaluation of the BDS shows that this scheme exhibits a delay improvement of 22.6%–40% relative to other existing schemes. View full abstract»

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  • A Newly Developed Safety-Critical Computer System for China Metro

    Page(s): 709 - 719
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    Applications of advanced electronic technologies have greatly increased the efficiency and performance of safety-critical computer systems. In addition, the architectural flexibility of these systems reduces the types of printed circuit boards they can use, thereby reducing difficulties with maintenance. A newly developed safety-critical computer system is presented in this paper. The system uses some advanced electronic technologies and can be reconfigured to be a triple-modular-redundant system or a dual-modular-duplex-redundant system for different applications. The system's architecture and fail-safe technologies are discussed, and its reliability, availability, maintainability, and safety (RAMS) are evaluated based on the Markov method. Based on these evaluations, the safety-critical computer system developed herein demonstrates great potential for rail use. View full abstract»

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  • Integrating Off-Board Cameras and Vehicle On-Board Localization for Pedestrian Safety

    Page(s): 720 - 730
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1526 KB) |  | HTML iconHTML  

    Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments, initially occluded or obscured dynamic objects can unpredictably enter the path of a vehicle. We propose a system that integrates a vision-based offboard pedestrian tracking subsystem with an onboard localization and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controller's field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. Alternatively, the system can be also applied to vehicles that have autonomous navigation capabilities, in which case, instead of alert lights, the vehicle is halted or redirected. We implemented and tested the proposed solution on an automated industrial vehicle under autonomous operation and on a human-driven vehicle in a full-scale production facility, over a period of four months. View full abstract»

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  • Freeway Travel-Time Information: Design and Real-Time Performance Using Spot-Speed Methods

    Page(s): 731 - 742
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    This paper shows that the precision of a freeway travel-time information system, in a real-time context, is not related solely to the accuracy of the measurement. Immediacy in reporting the information and forecasting capabilities play a role. Therefore, focusing only on the accuracy of the travel-time measurement is a myopic approach, which can lead to counterintuitive results. Specifically, it is claimed that, using travel times estimated with the traditional spot-speed midpoint algorithm, the performance of the real-time information system when evolving traffic conditions prevail is better than using much more accurate directly measured travel times (MTTs). Guidelines for an adequate configuration of the common parameters of the system are provided. In addition, real-time context enhancements for travel-time estimation methods based on punctual speed measurements are proposed. These are addressed by taking into account an easy and practical implementation. They have been proven to work well in an empirical application on a Spanish freeway. View full abstract»

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  • Single-Train Trajectory Optimization

    Page(s): 743 - 750
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (894 KB) |  | HTML iconHTML  

    An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results. View full abstract»

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Aims & Scope

The IEEE Transactions on ITS is concerned with the design, analysis, and control of information technology as it is applied to transportation systems. The IEEE ITS Transactions is focused on the numerous technical aspects of ITS technologies spanned by the IEEE. Transportation systems are invariably complex, and their complexity arises from many sources. Transportation systems can involve humans, vehicles, shipments, information technology, and the physical infrastructure, all interacting in complex ways. Many aspects of transportation systems are uncertain, dynamic and nonlinear, and such systems may be highly sensitive to perturbations. Controls can involve multiple agents that (and/or who) are distributed and hierarchical. Humans who invariably play critical roles in a transportation system have a diversity of objectives and a wide range of skills and education. Transportation systems are usually large-scale in nature and are invariably geographically distributed.

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Meet Our Editors

Editor-in-Chief
Fei-Yue Wang
Professor
University of Arizona